October 2025 arXiv papers — page 6

Sarah Kiden, Oriane Peter, Gisela Reyes-Cruz, Maira Klyshbekova

Evidence shows that text-to-image (T2I) models disproportionately reflect Western cultural norms, amplifying misrepresentation and harms to minority groups. However, evaluating cultural sensitivity is inherently complex due to its fluid and multifaceted nature. This paper draws on a state-of-the-art review and co-creation workshops involving 59 individuals f

Adolfo Arroyo-Rabasa, Sergio Conti

We prove that a locally integrable function $f:(a,b) \to \mathbb R$ must be affine if its mean oscillation, considered as a function of intervals, can be extended to a locally finite Borel measure. In particular, we show that any function $f$ satisfying the integro-differential identity $|Df|(I)=4\text{osc}(f,I)$ for all intervals $I \subset {(a,b)}$ must be

Kenneth Yang, Wen-Li Wei, Jen-Chun Lin

Parameter-Efficient Fine-Tuning (PEFT) has emerged as a key strategy for adapting large-scale pre-trained models to downstream tasks, but existing approaches face notable limitations. Addition-based methods, such as Adapters, introduce inference latency and engineering complexity, whereas selection-based methods like Gradient-based Parameter Selection (GPS)

S. T. Dougherty, C. Fernández-Córdoba

In this paper, we consider codes over finite fields, finite abelian groups, and finite Frobenius rings. For such codes, the complete weight enumerator and the Hamming weight enumerator serve as powerful tools. These two types of weight enumerators satisfy the MacWilliams relations. We define the weight enumerator of a code with respect to an equivalence rela

Fengwen Han, Tao Wang

We study the Dirichlet problem of the following discrete infinity Laplace equation on unbounded subgraphs \begin{equation*} \Delta_{\infty}u(x):=\inf_{y\sim x}u(y)+\sup_{y\sim x}u(y)-2u(x)=f(x). \end{equation*} For the homogeneous case ($f=0$), the existence and uniqueness of sublinear solutions are established. This result is applied to prove the existence

Núria Fagella, Leticia Pardo-Simón

This article surveys the impact of Eremenko and Lyubich's paper ''Examples of entire functions with pathological dynamics'', published in 1987 in the Journal of the LMS. Through a clever extension and use of classical approximation theorems, the authors constructed examples exhibiting behaviours previously unseen in holomorphic dynamics. Their work laid foun

Zijian Wang, Chang Xu

This paper introduces ThoughtProbe, a novel inference time framework that leverages the hidden reasoning features of Large Language Models (LLMs) to improve their reasoning performance. Unlike previous works that manipulate the hidden representations to steer LLM generation, we harness them as discriminative signals to guide the tree structured response spac

Hussein Aliu Sule, Abdulwakil Olawale Saba, Choo Yee Yu

Aquaculture is pivotal for global food security but faces significant challenges from infectious diseases, particularly those caused by Streptococcus species such as Streptococcus iniae and Streptococcus agalactiae. These pathogens induce severe systemic infections in various fish species, resulting in high morbidity and mortality rates. This review consolid

Bas Janssens, Benjamin Oudejans

Let $\Gamma$ be a discrete subgroup of a unimodular locally compact group $G$. In Math. Ann. 388, 4251-4305 (2024), it was shown that the $L_p$ norm of a Fourier multiplier $m$ on $\Gamma$ can be bounded locally by its $L_p$-norm on $G$, modulo a constant $c(A)$ which depends on the support $A$ of $m$. In the context where $G$ is a connected Lie group with L

Weijian Jian, Yajun Zhang, Dawei Liang, Chunyu Xie

The rapid advancement of Multimodal Large Language Models (MLLMs) has extended CLIP-based frameworks to produce powerful, universal embeddings for retrieval tasks. However, existing methods primarily focus on natural images, offering limited support for other crucial visual modalities such as videos and visual documents. To bridge this gap, we introduce Rzen

Fan-Hao Lin, Chi-Jui Sung, Chu-Hsiang Huang, Hui Chen

Accurate cross-band channel prediction is essential for 6G networks, particularly in the upper mid-band (FR3, 7-24 GHz), where penetration loss and blockage are severe. Although ray tracing (RT) provides high-fidelity modeling, it remains computationally intensive, and high-frequency data acquisition is costly. To address these challenges, we propose CIR-UNe

Hernán A. González, Jakob Salzer

We study detector operators measuring energy to a power $\Delta-2$ at null infinity in four-dimensional gauge theories and gravity. These operators transform as conformal primaries on the celestial sphere and provide a natural basis for describing energy-flux observables in scattering processes. Using the collinear factorization of scattering amplitudes, we

Yvon Bossut

We define weak stable Kim-forking, a notion that generalizes stable forking to the context of NSOP1 theories. We adapt some of the known results on stable forking to this context.

Wenjie Liu, Panos Papadimitratos

With the rise of location-based service (LBS) applications that rely on terrestrial and satellite infrastructures (e.g., GNSS and crowd-sourced Wi-Fi, Bluetooth, cellular, and IP databases) for positioning, ensuring their integrity and security is paramount. However, we demonstrate that these applications are susceptible to low-cost attacks (less than $50),

Anders Malthe Westerkam, Amélia Struyf, Dimitri Lederer, Troels Pedersen

Low-earth-orbit (LEO) satellite communication systems that use millimeter-wave frequencies rely on large antenna arrays with hybrid analog-digital architectures for rapid beam steering. LEO satellites are only visible from the ground for short periods of times (a few tens of minutes) due to their high orbital speeds. This paper presents a variational message

Thomas Schulik, Viswanatha Reddy Batchu, Ramesh Kumar Dharmapuri, Saran Gundlapalli

The automotive industry is currently undergoing a major transformation with respect to the Electric/Electronic (E/E) and software architecture, driven by a significant increase in the complexity of the technological stack within a vehicle. This complexity acts as a driving force for Software-Defined Vehicles (SDVs) leading to the evolution of the automotive

Bernd Bohnet, Rumen Dangovski, Kevin Swersky, Sherry Moore

The remarkable capabilities of Large Language Models (LLMs) often need to be tailored for specific applications, requiring the integration of new knowledge or the acquisition of new skills. While full fine-tuning is a powerful adaptation method, it is computationally expensive and can lead to a degradation of general reasoning abilities, a phenomenon known a

Si-Yu Xiao, Xin-Di Zhao, Tian-Hao Mao, Yi-Wei Wang

Accurate downhole depth measurement is essential for oil and gas well operations, directly influencing reservoir contact, production efficiency, and operational safety. Collar correlation using a casing collar locator (CCL) is fundamental for precise depth calibration. While neural network has achieved significant progress in collar recognition, preprocessin

Ali Norouzifar, Wil van der Aalst

Event logs extracted from information systems offer a rich foundation for understanding and improving business processes. In many real-world applications, it is possible to distinguish between desirable and undesirable process executions, where desirable traces reflect efficient or compliant behavior, and undesirable ones may involve inefficiencies, rule vio

Alireza Gharahighehi, Felipe Kenji Nakano, Xuehua Yang, Wenhan Cu

Recommender systems (RSs) are intelligent filtering methods that suggest items to users based on their inferred preferences, derived from their interaction history on the platform. Collaborative filtering-based RSs rely on users past interactions to generate recommendations. However, when a user is new to the platform, referred to as a cold-start user, there

Davide Spirito, Sergio Marras, Beatriz Martín-García

Trigonal tellurium (Te) has attracted researchers' attention due to its transport and optical properties, which include electrical magneto-chiral anisotropy, spin polarization and bulk photovoltaic effect. It is the anisotropic and chiral crystal structure of Te that drive these properties, so the determination of its crystallographic orientation and handedn

Ferdinand Genans, Antoine Godichon-Baggioni, François-Xavier Vialard, Olivier Wintenberger

Adding entropic regularization to Optimal Transport (OT) problems has become a standard approach for designing efficient and scalable solvers. However, regularization introduces a bias from the true solution. To mitigate this bias while still benefiting from the acceleration provided by regularization, a natural solver would adaptively decrease the regulariz

Marina V. Yashina, Alexander G. Tatashev

A discrete-time totally asymmetric simple exclusion process on a lattice with open boundaries is considered. There are particles of different types. The type of a particle is characterized by the probability that a particle moves to a vacant site and the probability that a particle occupying the rightmost site departs the system. An approximate approach to c

Arun Jose

Language models trained via outcome-based reinforcement learning (RL) to reason using chain-of-thought (CoT) have shown remarkable performance. Monitoring such a model's CoT may allow us to understand its intentions and detect potential malicious behavior. However, to be effective, this requires that CoTs are legible and faithful. We study CoT legibility acr

Benedikt Ebing, Christian Goldschmied, Goran Glavaš

In the absence of sizable training data for most world languages and NLP tasks, translation-based strategies such as translate-test -- evaluating on noisy source language data translated from the target language -- and translate-train -- training on noisy target language data translated from the source language -- have been established as competitive approac

Hervé Le Dret, Annie Raoult

We study liquid crystal models with bulk free energy from the point of view of the second law of thermodynamics. We formulate these models as objective internal variable models. Examples of application are given for the de Gennes free energy.

Ulrich Langer, Richard Löscher, Olaf Steinbach, Huidong Yang

We consider some boundary value tracking optimal control problem constrained by a Neumann boundary value problem for some elliptic partial differential equation where the control acts as right-hand side. This optimal control problem can be reformulated asa state-based variational problem that is the starting point for the finite element discretizion. In this

Yijia Wang, Yiqing Shen, Weiming Chen, Zhihai He

Existing image editing methods can handle simple editing instructions very well. To deal with complex editing instructions, they often need to jointly fine-tune the large language models (LLMs) and diffusion models (DMs), which involves very high computational complexity and training cost. To address this issue, we propose a new method, called \textbf{C}ompl

Ali Raza Jafree, Konark Jain, Nick Firoozye

We investigate the mechanisms by which medium-frequency trading agents are adversely selected by opportunistic high-frequency traders. We use reinforcement learning (RL) within a Hawkes Limit Order Book (LOB) model in order to replicate the behaviours of high-frequency market makers. In contrast to the classical models with exogenous price impact assumptions

Hao Cheng, Yanbo Jiang, Qingyuan Shi, Qingwen Meng

Effective, reliable, and efficient evaluation of autonomous driving safety is essential to demonstrate its trustworthiness. Criticality metrics provide an objective means of assessing safety. However, as existing metrics primarily target longitudinal conflicts, accurately quantifying the risks of lateral conflicts - prevalent in urban settings - remains chal

Thibaut L. François

The recent detection of high-redshift supermassive black holes with JWST has renewed interest in the processes driving black hole growth. At the same time, both simulations and observations point to a widespread population of off-center intermediate-mass black holes in dwarf galaxies. Their ability to merge outside galactic centers may play a key role in sha

Marco Romito, Leonardo Roveri

This paper investigates enhanced dissipation for a passive scalar advected by "very rough" horizontal shear flows, described by an advection-diffusion equation on the 2D torus. The authors extend results of Galeati and Gubinelli (2023) to generic flows in negative Besov spaces, proving that the dissipation rate increases to infinity as viscosity vanishes. Th

Maximilian Probst Gutenberg, Weixuan Yuan

Given an undirected graph $G=(V,E,w)$, a Gomory-Hu tree $T$ (Gomory and Hu, 1961) is a tree on $V$ that preserves all-pairs mincuts of $G$ exactly. We present a simple and efficient randomized reduction from Gomory-Hu trees to polylog maxflow computations. On unweighted graphs, our reduction reduces to maxflow computations on graphs of total instance size $\

Lu Bowen

Recent deep trajectory predictors (e.g., Jiang et al., 2023; Zhou et al., 2022) have achieved strong average accuracy but remain unreliable in complex long-tail driving scenarios. These limitations reveal the weakness of the prevailing "one-model-fits-all" paradigm, particularly in safety-critical urban contexts where simpler physics-based models can occasio

Yi-Long Lu, Jiajun Song, Wei Wang

A central architectural question for both biological and artificial intelligence is whether judgment relies on specialized modules or a unified, domain-general resource. While the discovery of decodable neural representations for distinct concepts in Large Language Models (LLMs) has suggested a modular architecture, whether these representations are truly in

Robert Pommeranz, Kevin Tebbe, Ralf Heynicke, Gerd Scholl

In this paper a modular and scalable architecture for heterogeneous swarm-based Counter Unmanned Aerial Systems (C-UASs) built on PX4-Autopilot and Robot Operating System 2 (ROS 2) framework is presented. The proposed architecture emphasizes seamless integration of hardware components by introducing independent ROS 2 nodes for each component of a Unmanned Ae

Benjamin Hamm, Yannick Kirchhoff, Maximilian Rokuss, Klaus Maier-Hein

The ODELIA Breast MRI Challenge 2025 addresses a critical issue in breast cancer screening: improving early detection through more efficient and accurate interpretation of breast MRI scans. Even though methods for general-purpose whole-body lesion segmentation as well as multi-time-point analysis exist, breast cancer detection remains highly challenging, lar

Marius Feldmann, Tobias Nöthlich, Felix Walter, Maximilian Nitsch

This whitepaper presents parts of the results of the REDMARS2 project conducted in 2021-2022, exploring the integration of Recursive Internetwork Architecture (RINA) concepts into Delay- and Disruption-Tolerant Networking (DTN) protocols. Using Bundle-in-Bundle Encapsulation (BIBE), we implemented scope-based separation mechanisms resulting in scalable DTNs.

Weiming Chen, Yijia Wang, Zhihan Zhu, Zhihai He

We consider the problem of ultra-low bit rate visual communication for remote vision analysis, human interactions and control in challenging scenarios with very low communication bandwidth, such as deep space exploration, battlefield intelligence, and robot navigation in complex environments. In this paper, we ask the following important question: can we acc

Xicheng Zhang, Yuanlong Zhao

We study a compound Poisson (random time-change) approximation for stochastic differential equations (SDEs) and stochastic Volterra equations whose coefficients may be merely measurable in time and may even exhibit integrable singularities. For an SDE driven by Brownian motion, we replace the time variable by the Poisson clock $\mathcal{N}_t^\varepsilon$ and

Jing-cheng Liu, Jia-jie Wang

For a Borel probability measure $\mu$ on $\mathbb{R}^{n}$, it is called a spectral measure if the Hilbert space $L^{2}(\mu)$ admits an orthogonal basis of exponential functions. In this paper, we study the spectrality of fractal measures generated by an iterated function system (IFS) with $m$-periodic alternating contraction ratios. Specifically, for fixed $

Yu-Chen Kuo, Yi-Ju Tseng

The inherent multimodality and heterogeneous temporal structures of medical data pose significant challenges for modeling. We propose MedM2T, a time-aware multimodal framework designed to address these complexities. MedM2T integrates: (i) Sparse Time Series Encoder to flexibly handle irregular and sparse time series, (ii) Hierarchical Time-Aware Fusion to ca

Jia-Ning Chen, Zong-Kuan Guo, Liang-Bi Wu

The quasinormal modes (QNMs) of a rotating quantum corrected black hole (RQCBH) are studied by employing the hyperboloidal framework for the scalar perturbation. This framework is used to cast the QNMs spectra problem into a two-dimensional eigenvalue problem, then the spectra are calculated by imposing the two-dimensional pseudo-spectral method. Based on th

Álvaro Silva, Alexandra Mendes, Ruben Martins

The Dafny verifier provides strong correctness guarantees but often requires numerous manual helper assertions, creating a significant barrier to adoption. We investigate the use of Large Language Models (LLMs) to automatically infer missing helper assertions in Dafny programs, with a primary focus on cases involving multiple missing assertions. To support t

Wenfeng Huang, Xiangyun Liao, Yinling Qian, Hao Liu

Surgical reconstruction of dynamic tissues from endoscopic videos is a crucial technology in robot-assisted surgery. The development of Neural Radiance Fields (NeRFs) has greatly advanced deformable tissue reconstruction, achieving high-quality results from video and image sequences. However, reconstructing deformable endoscopic scenes remains challenging du

Shuyi Chen, Panagiotis Oikonomou, Zhengchang Hua, Nikos Tziritas

Multi-access Edge Computing (MEC) delivers low-latency services by hosting applications near end-users. To promote sustainability, these systems are increasingly integrated with renewable Energy Harvesting (EH) technologies, enabling operation where grid electricity is unavailable. However, balancing the intermittent nature of harvested energy with dynamic u

Zijia An, Boyu Diao, Ruiqi Liu, Libo Huang

Recent studies have demonstrated that incorporating trainable prompts into pretrained models enables effective incremental learning. However, the application of prompts in incremental object detection (IOD) remains underexplored. Our study reveals that existing prompt-pool-based approaches assume disjoint class sets across incremental tasks, which are unsuit

Sai Niranjan Ramachandran, Manish Krishan Lal, Suvrit Sra

We analyse how the sampling dynamics of distributions evolve in score-based diffusion models using cross-fluctuations, a centered-moment statistic from statistical physics. Specifically, we show that starting from an unbiased isotropic normal distribution, samples undergo sharp, discrete transitions, eventually forming distinct events of a desired distributi

Alvee Hassan, Rusab Sarmun, Muhammad E. H. Chowdhury, M Murugappan

Early detection of coronary artery disease (CAD) is critical for reducing mortality and improving patient treatment planning. While angiographic image analysis from X-rays is a common and cost-effective method for identifying cardiac abnormalities, including stenotic coronary arteries, poor image quality can significantly impede clinical diagnosis. We presen

Tim Buchholz, Marlis Hochbruck

We propose a novel non-iterative domain decomposition time integrator for acoustic wave equations using a discontinuous Galerkin discretization in space. It is based on a local Crank-Nicolson approximation combined with a suitable local prediction step in time. In contrast to earlier work using linear continuous finite elements with mass lumping, the propose

Gaby Maroun, Salah Eddine Bekhouche, Fadi Dornaika

Age estimation from facial images is a complex and multifaceted challenge in computer vision. In this study, we present a novel hybrid architecture that combines ConvNeXt, a state-of-the-art advancement of convolutional neural networks (CNNs), with Vision Transformers (ViT). While each model independently delivers excellent performance on a variety of tasks,

Xiaotian Xu, Wuxiao Wen, Tao Yang, Xin Zhang

In this work we obtain the exact solution of quantum integrable system associated with the Lie superalgebra $\mathfrak{gl}(1|1)$, both for periodic and for generic open boundary conditions. By means of the fusion technique we derive a closed set of operator identities among the fused transfer matrices. These identities allow us to determine the complete ener

I Gusti Ngurah Yudi Handayana, Ya-Tang Yu, Wei-Hsuan Chung, H. H. Jen

Waveguide quantum electrodynamics (wQED) has become a central platform for studying collective light-matter interactions in low-dimensional photonic environments. While conventional wQED systems rely on uniform chirality or reciprocal emitter-waveguide coupling, we propose a structured wQED framework, where the coupling directionality of each emitter can be

Hugo Bellezza, Marine Poizat, Olivier Vincent

We study the response of materials with nanoscale pores containing sodium chloride solutions, to cycles of relative humidity (RH). Compared to pure fluids, we show that these sorption isotherms display much wider hysteresis, with a shape determined by salt crystallization and deliquescence rather than capillary condensation and Kelvin evaporation. Both deliq

B. Pereira, S. Gonzalez-Gaitan, A. M. Mourao, J. Rino-Silvestre

We investigate the polarization patterns from the moonlit sky as observed from the European Southern Observatory at Cerro Paranal. The moonlit sky background can be significant in astronomical observations and thus be a source of contamination in polarimetric studies. Based on sky observations during full Moon with FORS2 in imaging polarimetric mode, we meas

Mingcui Zhang, Zhigang Jia

Medical images play a crucial role in assisting diagnosis, remote consultation, and academic research. However, during the transmission and sharing process, they face serious risks of copyright ownership and content tampering. Therefore, protecting medical images is of great importance. As an effective means of image copyright protection, zero-watermarking t

Shobhit Singhal, Lesia Mitridati

As distributed energy resources (DERs) proliferate, future power system will need new market platforms enabling prosumers to trade various electricity and grid-support products. However, prosumers often exhibit complex, product interdependent preferences and face limited cognitive and computational resources, hindering engagement with complex market structur

Maria Ramard, Romain Laniel, Mathieu Miroir, Olivier Kerbrat

Laser cutting is an old and multi-physical process that was quickly adopted by the metallurgical industry. However, this fast industrialisation has had a significant impact on quality control. Several studies have been carried out to characterise and minimise different types of cutting defects. Reviews published between 2008 and 2022 highlight that research

Rodrigo Matos Carnier, Laura Lahesoo, Kensuke Fukuda

With the growing volume of Internet of Things (IoT) network traffic, machine learning (ML)-based anomaly detection is more relevant than ever. Traditional batch learning models face challenges such as high maintenance and poor adaptability to rapid anomaly changes, known as concept drift. In contrast, streaming learning integrates online and incremental lear

Lifang Feng, Bin Pei, Yong Xu

This paper aims to explore non-Markovian dynamics of nonlinear dynamical systems subjected to fractional Gaussian noise (FGN) and Gaussian white noise (GWN). A novel memory-dependent Fokker-Planck-Kolmogorov (memFPK) equation is developed to characterize the probability structure in such non-Markovian systems. The main challenge in this research comes from t

Elvin Rada

We propose and analyze a multi-inertial-iteration scheme in cone b, p-normed Banach spaces. This framework extends the classical Krasnoselskii-Mann and two-step inertial iterations by incorporating three independent inertial parameters and multiple error-control sequences. Under mild assumptions such as quasi-nonexpansiveness, weak contraction, and compatibi

Kirill A. Chertoganov, Valery I. Shipalov

The paper considers the numerical solution of nonlinear integral equations using the Newton-Kantorovich method with the mpmath library. High-precision quadrature of the kernel K(t, s, u) with respect to the variable s for fixed t increases stability and accuracy in problems sensitive to rounding and dispersion. The presented implementation surpasses traditio

Andrey Babichev, Alexey Blokhin, Yuriy Zadiranov, Yulia Salii

High-quality micropillar cavities were grown using molecular-beam epitaxy. Stable continuous-wave lasing at room-temperature was demonstrated for microlasers with semiconductor and hybrid output mirrors. At 300 K, single-mode lasing was demonstrated for micropillars with a diameter of 5 $\mu$m at a wavelength of 960 nm, with a minimum lasing threshold of 1.2

Valeria Grisoni, Federico Rizzuti, Gabriele Cescutti

The chemical evolution of fluorine is still a matter of debate in Galactic archaeology, especially at low metallicities, where it is particularly challenging to obtain the corresponding chemical abundances from observations. We present here the first detailed theoretical study of the chemical evolution of fluorine at low metallicity by means of a stochastic

Rebeka Toth, Richard A. Dubniczky, Olga Limonova, Norbert Tihanyi

Phishing constitutes more than 90\% of successful cyberattacks globally, remaining one of the most persistent threats to organizational security. Despite organizations tripling their cybersecurity budgets between 2015 and 2025, the human factor continues to pose a critical vulnerability. This study presents a 12-month longitudinal investigation examining how

Berken Utku Demirel, Christian Holz

The oscillations of the human heart rate are inherently complex and non-linear -- they are best described by mathematical chaos, and they present a challenge when applied to the practical domain of cardiovascular health monitoring in everyday life. In this work, we study the non-linear chaotic behavior of heart rate through mutual information and introduce a

Wenfeng Huang, Xiangyun Liao, Wei Cao, Wenjing Jia

Medical image super-resolution (SR) is essential for enhancing diagnostic accuracy while reducing acquisition cost and scanning time. However, modeling both long-range anatomical structures and fine-grained frequency details with low computational overhead remains challenging. We propose FGMamba, a novel frequency-aware gated state-space model that unifies g

Charlotte Gehan

The fraction of low-mass (LM) stars (M <= 1.5 Msun) showing photospheric activity in their light curve is larger on the horizontal branch (HB) than during the previous red giant branch (RGB) phase, while the opposite trend has been observed for intermediate-mass (IM) stars (M > 1.5 Msun). One hypothesis is that LM red giants (RGs) engulf more planets than IM

L. J. Bannenberg, F. García-Martínez, P. Lömker, R. Y. Engel

Palladium hydride is a model system for studying metal-hydrogen interactions. Yet, its bulk electronic structure has proven difficult to directly probe, with most studies to date limited to surface-sensitive photoelectron spectroscopy approaches. This work reports the first in-situ ambient-pressure hard X-ray photoelectron spectroscopy (AP-HAXPES) study of h

David Cardona Ochoa, Marek Płoszajczak, Nicolas Michel, Simin Wang

The double pole singularity of the $S$-matrix, the so-called exceptional point, associated with the $5/2^-$ doublet of resonances in the spectrum of $^{7}$Be has been identified in the framework of the Gamow shell model. The exceptional point singularity is demonstrated by the coalescence of wave functions and spectral functions of the two resonances, as wel

Xiaoling Wang, Kuilin Wu

In this paper, the main purpose is to explore an SIRS epidemic model with a general nonlinear incidence rate $f(I)S=\beta I(1+\upsilon I^{k-1})S$ ($k>0$). We analyzed the existence and stability of equilibria of the epidemic model. Local bifurcation theory is applied to explore the rich variety of dynamical behavior of the model. Normal forms of the epidemic

Qiao Wang, Ming Li, Liang Gao, Qi Guo

We present the Hyper Millennium (HM) simulation, an extremely large cosmological simulation designed to support next-generation galaxy surveys. The simulation follows 4.2 trillion dark matter particles in a comoving box of $2.5\ h^{-1}{\rm Gpc}$, with a mass resolution of $3.2 \times 10^8\, {h^{-1}\rm M_{\odot}}$ and a force resolution of $3.0\ h^{-1}{\rm kp

Su Gao, Yingying Jiang, Tianhao Wang

In this paper we consider the Borel combinatorics of Schreier graphs of $\mathbb{Z}$-actions with arbitrary finite generating sets. We formulate the Borel combinatorics in terms of existence of Borel equivariant maps from $F(2^{\mathbb{Z}})$ to subshifts of finite type. We then show that the Borel combinatorics and the continuous combinatorics coincide, and

Zhengchang Hua, Panagiotis Oikonomou, Karim Djemame, Nikos Tziritas

The coordination of large-scale, decentralised systems, such as a fleet of Electric Vehicles (EVs) in a Vehicle-to-Grid (V2G) network, presents a significant challenge for modern control systems. While collaborative Digital Twins have been proposed as a solution to manage such systems without compromising the privacy of individual agents, deriving globally o

Chen Xiao, Boyu Zhang, Xiangyu Zheng, Yuxuan Yao

Light pulses offer a faster, more energy-efficient, and direct route to magnetic bit writing, pointing toward a hybrid memory and computing paradigm based on photon transmission and spin retention. Yet progress remains hindered, as deterministic, single-pulse optical toggle switching has so far been achieved only with ferrimagnetic materials, which require t

Harsh Vishwakarma, Ankush Agarwal, Ojas Patil, Chaitanya Devaguptapu

Enterprise systems are crucial for enhancing productivity and decision-making among employees and customers. Integrating LLM based systems into enterprise systems enables intelligent automation, personalized experiences, and efficient information retrieval, driving operational efficiency and strategic growth. However, developing and evaluating such systems i

Saikat Basu, Suman Majumder, Raja Paul, Subir K. Das

Hydrodynamics is known to have strong effects on the kinetics of phase separation. There exist open questions on how such effects manifest in systems under confinement. Here, we have undertaken extensive studies of the kinetics of phase separation in a two-component fluid that is confined inside pores of cylindrical shape. Using a hydrodynamics-preserving th

Dominik John, Gregor Breitenhuber, Sami Wirtensohn, Franziska Hinterdobler

X-ray microtomography at synchrotron sources is fundamentally limited by the high radiation dose applied to the samples, which restricts investigations to non-native tissue states and thereby compromises the biological relevance of the resulting data. The limitation stems from inefficient indirect detection schemes that require prolonged exposures. Efforts t

Minghui Li, Yuanhang Wang, Peijin Guo, Wei Wan

Accurate prediction of Drug-Target Affinity (DTA) is crucial for reducing experimental costs and accelerating early screening in computational drug discovery. While sequence-based deep learning methods avoid reliance on costly 3D structures, they still overlook simultaneous modeling of global sequence semantic features and local topological structural featur

Zirui Zhu, Hailun Xu, Yang Luo, Yong Liu

Multimodal large language models (MLLMs) represent images and video frames as visual tokens. Scaling from single images to hour-long videos, however, inflates the token budget far beyond practical limits. Popular pipelines therefore either uniformly subsample or apply keyframe selection with retrieval-style scoring using smaller vision-language models. Howev

Qi Yan, Qingying Deng, Xian'an Jin

We prove a conjecture of Fomichev and Karev [{European J. Combin.} 127 (2025) 104160] by showing the equality of two graph invariants: $\varphi$, defined via graph colorings, and $\psi$, derived from the $\mathfrak{sl}(2)$-weight system of its 2-dimensional irreducible representation.

Alberto Enciso, Marc Nualart

We study the linear asymptotic stability of stably stratified monotone shear flows for the Boussinesq equations in the periodic channel. By means of the limiting absorption principle, we obtain a precise description of the inviscid damping experienced by the perturbed velocity field and density, with time-decay rates that depend on the local Richardson numbe

Francesco Romaggi

The main purpose of this article is to give a general overview and understanding of the first widely used option-pricing model, the Black-Scholes model. The history and context are presented, with the usefulness and implications in the economics world. A brief review of fundamental calculus concepts is introduced to derive and solve the model. The equation i

Tianchen Shao, Zekui Jia, Maokun Li, Shenheng Xu

The wave operator model provides a framework for modeling wave propagation by encoding material parameter distributions into matrix-form operators. This paper extends this framework from lossless to lossy media. We present a derivation of the wave operator solution for the electric field in dissipative environments, which can be decomposed into a closed-form

Yu Lin, Zhen Jia, Philipp Christmann, Xu Zhang

Drug recommendation (DR) systems aim to support healthcare professionals in selecting appropriate medications based on patients' medical conditions. State-of-the-art approaches utilize deep learning techniques for improving DR, but fall short in providing any insights on the derivation process of recommendations -- a critical limitation in such high-stake ap

Maurizio Palesi, Enrico Russo, Hamaad Rafique, Giuseppe Ascia

Scalable quantum computing requires modular multi-chip architectures integrating multiple quantum cores interconnected through quantum-coherent and classical links. The classical communication subsystem is critical for coordinating distributed control operations and supporting quantum protocols such as teleportation. In this work, we consider a realization b

Vincent K. M. Cheung, Pei-Cheng Shih, Masato Hirano, Masataka Goto

As people nowadays increasingly rely on artificial intelligence (AI) to curate information and make decisions, assigning the appropriate amount of trust in automated intelligent systems has become ever more important. However, current measurements of trust in automation still largely rely on self-reports that are subjective and disruptive to the user. Here,

Weiwen Huang, Li Liang, Ningsheng Xu, Fang Deng

In this study, we consider a multi-pursuer single-evader quantitative pursuit-evasion game with payoff function that includes only the terminal cost. The terminal cost is a function related only to the terminal position of the evader. This problem has been extensively studied in target defense games. Here, we prove that a candidate for the value function gen

Yida Zhang, Qiuyan Liu, Yuqi Xia, Guoxu Xia

To further suppress the inherent self-interference (SI) in co-frequency and co-time full-duplex (CCFD) systems, we propose integrating a stacked intelligent metasurface (SIM) into the RF front-end to enhance signal processing in the wave domain. Furthermore, an end-to-end (E2E) learning-based signal processing method is adopted to control the metasurface. Sp

Deokhyung Kang, Seonjeong Hwang, Daehui Kim, Hyounghun Kim

Reasoning language models (RLMs) achieve strong performance on complex reasoning tasks, yet they still exhibit a multilingual reasoning gap, performing better in high-resource languages than in low-resource ones. While recent efforts have been made to address this gap, its underlying causes remain largely unexplored. In this work, we show that this gap prima

Dimitri Loutchko, Keisuke Sugie, Tetsuya J Kobayashi

This article serves to concisely review the link between gradient flow systems on hypergraphs and information geometry which has been established within the last five years. Gradient flow systems describe a wealth of physical phenomena and provide powerful analytical technquies which are based on the variational energy-dissipation principle. Modern nonequilb

Kangkun Mao, Jinru Ding, Jiayuan Chen, Mouxiao Bian

As large language models (LLMs) enter the medical domain, most benchmarks evaluate them on question answering or descriptive reasoning, overlooking quantitative reasoning critical to clinical decision-making. Existing datasets like MedCalc-Bench cover few calculation tasks and fail to reflect real-world computational scenarios. We introduce MedCalc-Eval, the

Raza Imam, Hu Wang, Dwarikanath Mahapatra, Mohammad Yaqub

In medical imaging, vision-language models face a critical duality: pretrained networks offer broad robustness but lack subtle, modality-specific characteristics, while fine-tuned expert models achieve high in-distribution accuracy yet falter under modality shift. Existing model-merging techniques, designed for natural-image benchmarks, are simple and effici

Junhyeong An, Soojoon Lee

Unlike classical correlations, entanglement cannot be freely shared among multiple parties. This unique feature of quantum systems is known as the monogamy of entanglement. While it holds for all multipartite pure states, its converse -- weak entanglement between two parties enforces strong entanglement with a third party -- occurs only under specific condit

Han Yu, Kehan Li, Dongbai Li, Yue He

Recently, there has been gradually more attention paid to Out-of-Distribution (OOD) performance prediction, whose goal is to predict the performance of trained models on unlabeled OOD test datasets, so that we could better leverage and deploy off-the-shelf trained models in risk-sensitive scenarios. Although progress has been made in this area, evaluation pr

Ran Xu, Yupeng Qi, Jingsen Feng, Xu Chu

In modern engineering practice, human engineers collaborate in specialized teams to design complex products, with each expert completing their respective tasks while communicating and exchanging results and data with one another. While this division of expertise is essential for managing multidisciplinary complexity, it demands substantial development time a

Yinglu Li, Zhiying Lu, Zhihang Liu, Yiwei Sun

Multi-modal Retrieval-Augmented Generation (RAG) has become a critical method for empowering LLMs by leveraging candidate visual documents. However, current methods consider the entire document as the basic retrieval unit, introducing substantial irrelevant visual content in two ways: 1) Relevant documents often contain large regions unrelated to the query,

Doulaye Dembélé

Classification is a machine learning method used in many practical applications: text mining, handwritten character recognition, face recognition, pattern classification, scene labeling, computer vision, natural langage processing. A classifier prediction results and training set information are often used to get a contingency table which is used to quantify

Anuradha Jagannathan

Many new families of quasicrystal-forming magnetic alloys have been synthesized and studied in recent years. For small changes of composition, the alloys can go from quasiperiodic to periodic (approximant crystals) while conserving most of the local atomic environments. Experiments show that many of the periodic approximants order at low temperatures, with c

Muneer Ahmad, M. Sadik Batcha, Basharat Ahmad Wani, Mohammad Idrees Khan

Webology is an international peer-reviewed journal in English devoted to the field of the World Wide Web and serves as a forum for discussion and experimentation. It serves as a forum for new research in information dissemination and communication processes in general, and in the context of the World Wide Web in particular. This paper presents a Scientometri